TypeSafe's Jev is a new type of AI system that lives inside software and makes judgment calls, doing the background decision-making work that chatbots aren’t always the right tool for — at a wild speed and price point.NEW: Our Community AI Workflow Hub now takes questions. Stuck on a prompt, a tool, or a workflow that won't cooperate? Ask the community and get answers from people who've already figured it out.In today’s AI rundown:
- ChatGPT co-creator launches a new kind of AI
- Salesforce trains its own reasoning model
- OpenRouter 101: Add models to Codex, Claude Code
- Chinese researchers detail 'the last AI built by humans'
- Jev runs at $42 for a billion input tokens, and output is free (!), a rate TypeSafe estimates at 238x below Claude Fable 5.1's pricing.
- Responses also output in just 70 to 500 milliseconds, between 40-200x faster than today’s LLMs.
- TypeSafe says Jev “can’t hallucinate,” because it only chooses between options set in advance, calling the system a “frontier-intelligence function call.”
- TypeSafe’s Jev use cases include quick judgment calls inside apps, like sorting requests, scoring records, or screening another AI's outputs for jailbreaks.
The Rundown: Search results go stale, so teams bolt on an API, add a scraper, wire up a parser, and hope it holds. Yeah, it works in the demo, but then it breaks under production load six months later. This guide from You.com explains why treating research as simple retrieval is the wrong frame, and what a purpose-built stack looks like instead.What you'll learn:- Why general-purpose search hits 93% SimpleQA accuracy but still fails high-stakes workflows
- The difference between an API that returns results and one that answers questions
- How teams reclaim 6–12 months of time with a single endpoint
- What finance, legal, and compliance workflows need beyond web search
Image source: SalesforceThe Rundown: Salesforce just introduced Koa, a new in-house reasoning model for its sales and support agents built on Nvidia’s open Nemotron 3 Super model and adapted for business tasks. The details:- The training set is fully synthetic, simulating personas like irate support callers and reps closing deals across over a dozen industries, without using customer data.
- Koa made 3x fewer errors than top models on an internal CRM benchmark, matching or beating them on tasks like updating deals or routing tickets.
- The company hosts Koa itself, keeping customer requests inside its systems instead of sending them to an outside model provider.
- Salesforce also launched AIforce, which pushes CRM data and permissions into outside AI tools, and opened its ClaudeForce version to every customer as a beta.
The Rundown: In this guide, you will learn how to give your AI agent more models to work with using OpenRouter. We'll set up an API key, compare models, and test cheaper options for making ad mockups.Step-by-step:- Sign in to OpenRouter and browse the model catalog. Filter for your task, then sort by low to high price. Open a model page to check its capabilities and cost
- Open API Keys, choose New key, and name it after the project or agent you'll use it with. Set a spending limit and an expiration date before creating it
- Ask your agent to help you set up OpenRouter. Store the key privately using its setup instructions, then give it the model name and a small task. We asked our bot to make more hat ad mockups with a cheaper image model
- Compare the results and check usage in OpenRouter. Our mockups were useful for direction, but they didn't match the real hat or the GPT Image 2.5 version
The Rundown: AI transformation fails when you DIY. StackAI gives you one platform to build, deploy, and scale, backed by a team of dedicated AI experts who know what works. IT retains 100% control over access, tokens, and usage.Why enterprises choose StackAI:- No-code platform for rapid building and deployment
- 300+ native integrations, no LLM lock-in
- Global governance and advanced analytics
Image source: arXivThe Rundown: 30+ Chinese AI researchers, including those from ByteDance, Tsinghua, and Shanghai AI Lab, published “The Last AI Built by Humans,” a roadmap detailing five levels of recursive self-improvement, ending with AI that builds its successors.The details: - Level 1 has the AI carrying out upgrades humans designed, while Level 2 lets it diagnose its own weak spots and decide how to fix them.
- Levels 3 and 4 hand over what the model learns next and how it adapts after launch, and Level 5 lets it completely overhaul the improvement process itself.
- The authors say coding has the clearest path to RSI, since fixes can be tested instantly, while robotics, science, and medicine face slower, costly feedback.
- The researchers also sorted 491 existing papers onto the ladder, with 75% landing at Level 1 or 2, and under 6% making it to Level 5.
Koa - Salesforce’s CRM reasoning model built for enterprise work
Gemini 3.8 Live - Google's new voice models that think while they talk
StepAudio 3 - Five-model audio suite for voice agents, transcription, music
Jev - TypeSafe's early-access AI for software
- Read our last AI newsletter: Trump, China both shoot down the AI slowdown
- Read our last Tech newsletter: U.S. confirms weapons are in orbit
- Read our last Robotics newsletter: Japan’s latest answer to home robots
- Today’s AI tool guide: OpenRouter 101: Add models to Codex, Claude Code
- RSVP to next workshop on Sept. 30: Turn Cowork into your chief of staff
Source: https://therundownai.beehiiv.com/p/chat ... kind-of-ai